scholarly journals Dynamic integration of conceptual information during learning

2018 ◽  
Author(s):  
Marika C. Inhoff ◽  
Laura A. Libby ◽  
Takao Noguchi ◽  
Bradley C. Love ◽  
Charan Ranganath
Keyword(s):  
Computational Model ◽  
Learning Task ◽  
Higher Order ◽  
Neural Mechanisms ◽  
Semantic Features ◽  
Learning Problem ◽  
Conceptual Information ◽  
Conceptual Representations ◽  
Model Based ◽  
Learning Rates

AbstractThe development and application of concepts is a critical component of cognition. Although concepts can be formed on the basis of simple perceptual or semantic features, conceptual representations can also capitalize on similarities across feature relationships. By representing these types of higher-order relationships, concepts can simplify the learning problem and facilitate decisions. Despite this, little is known about the neural mechanisms that support the construction and deployment of these kinds of higher-order concepts during learning. To address this question, we combined a carefully designed associative learning task with computational model-based functional magnetic resonance imaging (fMRI). Participants were scanned as they learned and made decisions about sixteen pairs of cues and associated outcomes. Associations were structured such that individual cues shared feature relationships, operationalized as shared patterns of cue pair-outcome associations. In order to capture the large number of possible conceptual representational structures that participants might employ and to evaluate how conceptual representations are used during learning, we leveraged a well-specified Bayesian computational model of category learning [1]. Behavioral and model-based results revealed that participants who displayed a tendency to link experiences in memory benefitted from faster learning rates, suggesting that the use of the conceptual structure in the task facilitated decisions about cue pair-outcome associations. Model-based fMRI analyses revealed that trial-by-trial integration of cue information into higher-order conceptual representations was supported by an anterior temporal (AT) network of regions previously implicated in representing complex conjunctions of features and meaning-based information.

scholarly journals Model-based aversive learning in humans is supported by preferential task state reactivation

2021 ◽  
Vol 7 (31) ◽  
pp. eabf9616
Author(s):  
Toby Wise ◽  
Yunzhe Liu ◽  
Fatima Chowdhury ◽  
Raymond J. Dolan
Keyword(s):  
Mental Model ◽  
Learning Task ◽  
Harm Avoidance ◽  
Neural Mechanisms ◽  
Trial And Error ◽  
Aversive Learning ◽  
Theta Power ◽  
Model Based ◽  
Outcome Valence ◽  
Hippocampal Theta

Harm avoidance is critical for survival, yet little is known regarding the neural mechanisms supporting avoidance in the absence of trial-and-error experience. Flexible avoidance may be supported by a mental model (i.e., model-based), a process for which neural reactivation and sequential replay have emerged as candidate mechanisms. During an aversive learning task, combined with magnetoencephalography, we show prospective and retrospective reactivation during planning and learning, respectively, coupled to evidence for sequential replay. Specifically, when individuals plan in an aversive context, we find preferential reactivation of subsequently chosen goal states. Stronger reactivation is associated with greater hippocampal theta power. At outcome receipt, unchosen goal states are reactivated regardless of outcome valence. Replay of paths leading to goal states was modulated by outcome valence, with aversive outcomes associated with stronger reverse replay than safe outcomes. Our findings are suggestive of avoidance involving simulation of unexperienced states through hippocampally mediated reactivation and replay.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

scholarly journals A computational model-based approach for atlas construction of aortic Doppler velocity profiles for segmentation purposes

2018 ◽  
Vol 40 ◽  
pp. 23-32 ◽  
Author(s):  
Vedrana Baličević ◽  
Hrvoje Kalinić ◽  
Sven Lončarić ◽  
Maja Čikeš ◽  
Bart Bijnens
Keyword(s):  
Computational Model ◽  
Velocity Profiles ◽  
Doppler Velocity ◽  
Model Based ◽  
Atlas Construction

Higher-order computational model for coded aperture spectral imaging

2013 ◽  
Vol 52 (10) ◽  
pp. D12 ◽  
Author(s):  
Henry Arguello ◽  
Hoover Rueda ◽  
Yuehao Wu ◽  
Dennis W. Prather ◽  
Gonzalo R. Arce
Keyword(s):  
Computational Model ◽  
Spectral Imaging ◽  
Higher Order ◽  
Coded Aperture

Knowledge Acquired from Learning: New Evidence of Hierarchical Conceptualization

1994 ◽  
Vol 79 (2) ◽  
pp. 975-993 ◽  
Author(s):  
Alberto Montare
Keyword(s):  
Sex Differences ◽  
Knowledge Acquisition ◽  
Reversal Learning ◽  
Concept Formation ◽  
Learning Task ◽  
Learning Rates ◽  
Level 3 ◽  
Level 1 ◽  
New Evidence ◽  
Level 2

Following successful inductive acquisition of procedural cognition of a discrimination-reversal learning task, 50 female and 50 male undergraduates articulated declarative cognizance of knowledge acquired from learning. Tests of four hypotheses showed that (1) increasingly higher levels of declarative cognizance were associated with faster learning rates, (2) six new cases of cognition-without-cognizance were observed, (3) students presumably using secondary signalization learned faster than those presumably using primary signalization, and (4) no sex differences in learning rates or declarative cognizance were observed. The notion that explicit levels of declarative cognizance may represent implicit hierarchical conceptualization comprised of four systems of knowledge acquisition led to the conclusions that primary signalization may account for inductive senscept formation at Level 1 and for inductive percept formation at Level 2, whereas emergent secondary signalization may account for inductive precept formation at Level 3 and for inductive concept formation at Level 4.

scholarly journals Sentiment Analysis on Twitter Airline Data

2021 ◽  
Vol 9 (VI) ◽  
pp. 3767-3770
Author(s):  
Kirti Jain
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Task ◽  
Model Based ◽  
Sentiment Mining ◽  
General Opinion

Sentiment analysis, also known as sentiment mining, is a submachine learning task where we want to determine the overall sentiment of a particular document. With machine learning and natural language processing (NLP), we can extract the information of a text and try to classify it as positive, neutral, or negative according to its polarity. In this project, We are trying to classify Twitter tweets into positive, negative, and neutral sentiments by building a model based on probabilities. Twitter is a blogging website where people can quickly and spontaneously share their feelings by sending tweets limited to 140 characters. Because of its use of Twitter, it is a perfect source of data to get the latest general opinion on anything.

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